Vviewpoints

I M A G E B Y A N T O N K H R U P I N

DOI: 10.1145/3055277

mans actually do not understand intelligence very well in the first place. Now, computer scientists often think they understand intelligence because they have so often been the “smart” kid, but that’s got very little to do with understanding what intelligence actually is. In the absence of a clear understanding of how the human brain generates and evaluates ideas, which may or may not be a good basis for the concept of intelligence, we have introduced numerous proxies for intelligence, the first of which is game-playing behavior.

One of the early challenges in AI— and for the moment I am talking about AI in the large, not soft or weak or any other marketing buzzword—was to get a computer to play chess. Now, why would a bunch of computer scientists want to get a computer to play

Dear KV,

Our company is looking at handing much of our analytics to a company that claims to use “Soft AI” to get answers to questions about the data we have collected via our online sales system. I have been asked by management to evaluate this solution, and throughout the evaluation all I can see is that this company has put a slick interface on top of a pretty standard set of analytical models. I think what they really mean to say is “Weak AI” and that they’re using the term Soft so they can trademark it. What is the real difference between soft (or weak) AI and AI in general?

Feeling Artificially Dumb
Dear AD,

The topic of AI hits the news about every 10 to 20 years, whenever a new level of computing performance becomes so broadly deployed as to enable some new type of application. In the 1980s it was all about expert systems. Now we see advances in remote control (such as military drones) and statistical number crunching (search engines, voice menus, and the like).

The idea of artificial intelligence is no longer new, and, in fact, the thought that we would like to meet and interact with non-humans has existed in fiction for hundreds of years. Ideas about AI that have come out of the 20th century have some well-known sources—including the writings of Alan Turing and Isaac Asimov.

Turing’s scientific work generated the
now-famous Turing test, by which a
machine intelligence would be judged
against a human one; and Asimov’s
fiction gave us the Three Laws of Ro-
botics, ethical rules that were to be
coded into the lowest-level software
of robotic brains. The effects of the
latter on modern culture, both tech-
nological and popular, are easy to
gauge, since newspapers still discuss
advances in computing with respect
to the three laws. The Turing test is,
of course, known to anyone involved
in computing, perhaps better known
than the halting problem (https://
en.wikipedia.org/wiki/Halting_prob-
lem), much to the chagrin of those of
us who deal with people wanting to
write “compiler-checking compilers.”
The problem inherent in almost
all nonspecialist work in AI is that hu-

Kode Vicious
The Chess Player Who
Couldn’t Pass the Salt
AI: Soft and hard, weak and strong, narrow and general.